LLM Regression Testing: Gates That Survive a Model Upgrade

LLM regression testing should focus on identifying which specific behaviors worsen after changes to models, prompts, tools, retrieval systems, or provider infrastructure, rather than relying on net pass-rate changes that can conceal substantial churn between newly improved and newly failing cases. Cited research, including non-peer-reviewed preprints, suggests that model upgrades can improve aggregate accuracy while degrading many individual items, and that one-shot evaluations can both miss reliable changes and create false alerts, supporting repeated sampling for critical scenarios despite its added cost. Because newer hosted models may not support fixed sampling controls and neither temperature zero nor pinned seeds fully guarantees deterministic output, teams should treat repeated identical results as uncertain, log model identifiers and available backend fingerprints, and distinguish provider-side configuration changes from product changes. Regression suites should report improved and degraded counts separately, block critical pass-to-fail regressions, track cost, tokens, latency, and cache state against baseline bands, and incorporate provider deprecation and retirement dates into scheduled checks so model migrations can be tested while both versions remain available. When a gate fails, a practical triage sequence is to rerun unchanged inputs, inspect provider metadata, test the prior build against the current endpoint, and then isolate changes in prompts, retrieval, or tooling, while documenting and periodically reviewing unstable cases that must be quarantined.

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